Optimal control of electrical vehicle incorporated hybrid power system with second order fractional-active disturbance rejection controller

This article proposes a novel control methodology employing a fractional-active-disturbance-rejection-controller for the combined operation of load frequency control and automatic voltage regulator of a hybrid power system. A two area hybrid power system with diverse energy sources like solar-thermal, conventional-thermal and wind sources equipped with appropriate system nonlinearities is investigated. In order to ascertain the role of modern-day electric-vehicle (EV), the hybrid power system is incorporated with EVs in both the areas. To establish an effective frequency, voltage and tie line power control of the hybrid power system, a second order fractional-active-disturbance-rejection-controller with fractional-extended state observer is modeled as secondary controller. Magnetotactic-bacteria-optimization (MBO) technique is applied to obtain optimal values of the controller gains and the hybrid system parameters. The robustness of the controller gains is tested under different system parameter changes from their nominal values. In addition, the effect of incorporating a power system stabilizer on the hybrid power system is evaluated. Further, the impact of integrating renewable sources and EVs in the hybrid power system is explored. Moreover, the stability of the hybrid power system is monitored with the inclusion of FACTS device. The developed controller operates encouragingly with reference to system stability, rapidity and accuracy in comparison to testified control strategies available in the literature. The robustness test under load-perturbation, solar-insolation, wind input variations also proves the efficiency of MBO optimized second order fractional-active-disturbance-rejection-controller gains.     

Adaptive fuzzy finite-time optimal control for switched nonlinear systems

This article addresses the adaptive fuzzy finite-time control problem for a class of switched nonlinear systems whose powers are positive odd rational numbers and vary with the switching signal. The fuzzy logic systems (FLSs) are used to approximate the unknown nonlinearities of the controlled systems, and then by combining backstepping control algorithm with adding a power integrator technique, an adaptive finite-time controller is designed. By modifying and optimizing the relevant design parameters of the proposed adaptive finite-time controller, a novel adaptive finite-time optimal control approach is developed. The proposed two adaptive finite-time optimal control schemes can guarantee semi-globally practically finite-time stability of the closed-loop system. Moreover, the adaptive finite-time optimal controller can also achieve optimized performance in relation to the cost functional. Finally, a simulation example is given to illustrate the effectiveness of the proposed two adaptive finite-time control strategies.     

Optimal digital redesign of cascaded analogue controllers

This paper presents a new, optimal digital redesign technique for finding an optimal cascaded digital controller from the given continuous-time counterpart by minimizing a quadratic performance index. The control gains can be obtained by solving a set of Lyapunov equations. The developed optimal cascaded digital controller enables the state and/or outputs of the digitally controlled closed-loop sampled-data system to optimally match those of the original continuous-time closed-loop system at any instant between sampling periods. The developed control law can be implemented using inexpensive and reliable digital electronics with a relatively long sampling period.     

Controller design for optimal tracking response in discrete‐time systems

The problem of designing a controller, which results in a closed‐loop system response with optimal time‐domain characteristics, is considered. In the approach presented in this paper, the controller order is fixed (higher than pole‐placement order) and we seek a controller that results in closed‐loop poles at certain desired and pre‐specified locations; while at the same time the output tracks the reference input in an optimal way. The optimality is measured by requiring certain norms on the error sequence—between the reference and output signals—to be minimum. Several norms are used. First, l₂‐norm is used and the optimal solution is computed in one step of calculations. Second, l_∞‐norm (i.e. minimal overshot) is considered and the solution is obtained by solving a constrained affine minimax optimization problem. Third, the l₁‐norm (which corresponds to the integral absolute error‐(IAE)‐criterion) is used and linear programming techniques are utilized to solve the problem. The important case of finite settling time (i.e. deadbeat response) is studied as a special case. Examples that illustrate the different design algorithms and demonstrate their feasibility are presented. Copyright © 2007 John Wiley & Sons, Ltd.     

Relations between frequency-dependent control and state weighting in LQG problems

This paper considers the relation between controller designs for the same linear system achieved with two different quadratic performance indices. These indices differ only to the extent that one has frequency weighting on the control whereas the other has the inverse frequency weighting on the state. The feedback controllers are constructed by augmenting the given plant and by solving a modified LQG problem for the augmented system. The main result is that whereas the optimal controls as time functions can be the same, the optimal controllers are different. Filtering results are also obtained.     

Energy-efficient cabin climate control of electric vehicles using linear time-varying model predictive control

A cabin climate control system, often referred to as a heating, ventilation, and air conditioning (HVAC) system, is one of the largest auxiliary loads of an electric vehicle (EV), and the real-time optimal control of HVAC brings a significant energy-saving potential. In this article, a linear-time-varying (LTV) model predictive control (MPC)-based approach is presented for energy-efficient cabin climate control of EVs. A modification is made to the cost function in the considered MPC problem to simplify the Hessian matrix in utilizing quadratic programming for real-time computation. A rigorous parametric study is conducted to determine optimal weighting factors that work robustly under various operating conditions. Then, the performance of the proposed LTV-MPC controller is compared against a rule-based (RB) controller and a nonlinear economic MPC (NEMPC) benchmark. Compared with the RB controller benchmark, the LTV-MPC reaches the target cabin temperature at least 69 s faster with 3.2% to 15% less HVAC system energy consumption, and the averaged cabin temperature difference is 0.7°C at most. Compared with the NEMPC, the LTV-MPC controller can achieve comparable performance in temperature regulation and energy consumption with fast computation time: the maximum differences in temperature and energy consumption are 0.4°C and 2.6%, respectively, and the computational time is reduced 72.4% on average with the LTV-MPC.     

Optimal decentralized control of a wind turbine and diesel generator system

This article presents a decentralized optimal controller design technique for the frequency and power control of a coupled wind turbine and diesel generator. The decentralized controller consists of two proportional-integral (PI)-lead controllers which are designed and optimized simultaneously using a quasi-Newton based optimization technique, namely, Davidon–Fletcher–Powell algorithm. The optimal PI-lead controllers are designed in such a way that there are no communication links between them. Simulation results show the superior performance of the proposed controller with a lower order structure compared to the benchmark decentralized linear-quadratic Gaussian integral controllers of orders 4 and 11. It is also shown that the proposed controller demonstrates an effective performance in damping the disturbances from load and wind power, as well as a robust performance against the parameter changes of the power system.     

Multicriteria optimal operation of a microgrid considering risk analysis,renewable resources,and model predictive control

This paper proposes an optimal power dispatch by taking into account risk management and renewable resources. In particular, it examines how control engineering and risk management techniques can be applied in the field of power systems through their use in the design of risk-based model predictive controllers. To this end, this paper proposes a two-layer control scheme for microgrid management where both levels are based on model predictive control (MPC): the higher level is devoted to risk management while the lower layer is dedicated to power dispatching. In particular, the high-level controller is based on a risk-based approach where potential risks have been identified and evaluated. Mitigation actions are the decision variables to be optimized to reduce the consequences of risks and costs. The MPC-based algorithm decides the appropriate frequency of mitigation actions such as changes in references, constraints, and insurance contracting, by relying on a model that includes integer variables, identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. On the other hand, the low-level controller drives the plant to suitable values to satisfy demands. A series of simulations on a nonlinear model of a real laboratory-scale power plant located in the facilities of the University of Seville are conducted under varying conditions to demonstrate the effectiveness of the algorithm when risks are explicitly considered.     

An optimal dynamic output feedback controller for a flexible spacecraft

The design of a low-order dynamic output feedback controller for a flexible spacecraft is carried out using modified linear quadratic Gaussian (LQG) theory. The necessary conditions for optimization are given. The linear equations governing a flexible spacecraft with a central rigid body and two sun-tracking solar panels are presented. The parameters of the Solar Electric Propulsion Spacecraft (SEPS) are selected for analysis and simulation. The optimal gains for the dynamic controller are estimated using an iterative algorithm. The sequential procedure which assures convergence is selected. The initial gains which stabilize the system are chosen on the basis of the principle of a PD controller. A third-order controller for pitch, roll and yaw axes of the 18th-order system, which includes sensor and actuator dynamics, is designed. Numerical simulations carried out to ascertain the performance of the controller show the performance to be satisfactory.     

A strategy for power system stabilization by control-effort and response-time minimization

A method is described for deriving a quasi-optimal feedback control scheme for stabilizing power systems. This is achieved by minimizing a functional which is a linear combination of response time and control effort. The transient repsonse of a power system obtained using this control strategy is found to be superior to that achieved with a controller based on a linear regulator approach. This scheme, in a slightly modified form, is easily implemented in practice. The modification does not affect the effectiveness of the scheme to control transients. Simulation results are included.     

A decoupling pole placement self-tuning controller for a class of multivariable processes

This paper describes a new multivariable self-tuning controller that specifically handles different time delays between each of the input-output pairs (multiple delays), and deals with unknown or varying time delay (variable dead time compensation) without requiring an explicit estimate of each delay. The new algorithm can control unstable and/or non-minimum phase processes; it eliminates both set-point and load offsets without having to maintain integral action continuously; it decouples the loops, both dynamically and at steady state. The effective decoupling algorithm not only minimizes significant interactions among the control loops, it permits the use of a very simple design criterion, namely the approximate assignment of the primary closed-loop poles to prespecified locations. A straightforward autotuning technique locates the closed-loop poles on-line so as to optimize the system set-point step responses. One desirable side-effect is to account for inexact decoupling. The controller is demonstrated using a simulated paper machine head box having an unusually large amount of (off-diagonal) interaction, a simulated two-input, two-output distillation column with time-varying parameters (varying gains and time delays), and a highly interacting unstable and non-minimum phase system on which the autotuning technique is demonstrated. These three processes place stringent requirements on any control method, and two of them have been used extensively in the literature to test various multivariable and/or self-tuning algorithms. Hence, the success of the new technique indicates that it is a very promising candidate to deal with intransigent processes, i.e. those characterized as unstable, non-minimum phase, time-varying (non-linear), and interacting.     

Restricted structure predictive optimal control

The design of low‐order predictive optimal controllers, that involve a multi‐step cost index and future setpoint knowledge, is considered. The usual predictive controller is of high order and the aim is to develop simpler structures, suitable for applications where PID controllers might be employed. The system is assumed to be represented by a discrete‐time state‐space model, which is very general, and the quadratic cost‐function may include dynamic cost weighting terms. Using this approach, it is straightforward to generate a much lower‐order predictive controller and thereby simplify implementation. Even with a continuing improvement in computational power there are many good reasons why low‐order simple controllers have advantages in real applications. Copyright © 2004 John Wiley & Sons, Ltd.     

Improving the grid frequency by optimal design of model predictive control with energy storage devices

This paper proposes the optimal design of model predictive control (MPC) with energy storage devices by the bat‐inspired algorithm (BIA) as a new artificial intelligence technique. Bat‐inspired algorithm‐based coordinated design of MPCs with superconducting magnetic energy storage (SMES) and capacitive energy storage (CES) is proposed for load frequency control. Three‐area hydrothermal interconnected power system installed with MPC and SMES is considered to carry out this study. The proposed design procedure can account for generation rate constraints and governor dead bands. Transport time delays imposed by governors, thermodynamic processes, and communication telemetry can be captured as well. In recent papers, the parameters of MPC with SMES and CES units are typically set by trial and error or by the designer's expertise. This problem is solved here by applying BIA to tune the parameters of MPC with SMES and CES units simultaneously to minimize the deviations of frequency and tie line powers against load perturbations. Simulation results are carried out to emphasize the superiority of the proposed coordinated design as compared with conventional proportional‐integral controller and with BIA‐based MPC without SMES and CES units.     

ROBUST POWER SYSTEM LOAD-FREQUENCY CONTROLLER DESIGN BASED ON H∞ OPTIMIZATION APPROACH

An H_∞ controller based on the standard H_∞ control design approach is proposed for power system load-frequency control with system parametric uncertainties. The variation bounds of power system parameters are obtained by changing parameters by 30%-50% simultaneously from their typical values. The existing H_∞ control technique is applied to design a load-frequency controller for power systems. The design procedure is given. The proposed H_∞ controller is simple, effective and can ensure that the overall system is asymptotically stable for all admissible uncertainties. Our simulation results show that for the example system the proposed H_∞ load-frequency controller can achieve good performance even in the presence of generation rate constraint.     

Guaranteed-cost stabilization of non-linear stochastic systems

The control of uncertain non-linear discrete-time systems having stochastic cone-bounded non-linearities is considered. First, a quadratic performance bound and a guaranteed-cost optimal state feedback controller are derived. Then, an auxiliary system is introduced. It is shown that the quadratic optimal control for this auxiliary system is the same as the guaranteed-cost control for the original system, and, therefore, the existence of the infinite-horizon guaranteed-cost controller can be based on the stabilizability and observability properties of the auxiliary system. Finally, the stochastic boundedness of the controlled uncertain system is proved based on the properties of the auxiliary system. © 1998 John Wiley & Sons, Ltd.     

Cost function optimization for predictive control of a five-phase IM drive

Model predictive control has been used, for some time now, as a method to directly control power converters in electrical systems. The usual practice is tuning the cost function of the controller to obtain a certain compromise solution over the whole operating range of the system. This method is extended here to consider multiple, locally optimal, and tunings. The design objectives (tracking error, switching frequency, etc) are used to define a unique performance index that is locally optimized. In this way, the parameters of the cost function are linked to the current operating point. The tuning at each operating point is obtained numerically solving the optimization of the performance index. Although the idea can be applied to induction machines with any number of phases, in this paper, a five-phase induction motor is considered for presentation. This system is a demanding case due to the extra number of phases compared with the traditional three-phase motor. Simulation and experimental results are presented to assess the proposed predictive controller.     

Optimal control of a magnetic bearing without bias flux using finite voltage

Conventional active magnetic bearings (AMB) are operated using a bias current (or flux) to achieve greater linearity and dynamic capability. Bias, however, results in undesirable rotating losses and consequent rotor heating. While control without bias flux is an attractive alternative, it is considerably more complex due to both force slew rate limitations and actuator non-linearity. In this paper, optimal control of a magnetic bearing without bias is investigated. A single-degree-of-freedom system consisting of a mass and two opposing electromagnets is considered. The optimal control problem is examined for a cost function that penalizes both poor regulation and rotational energy lost. Though a standard optimization procedure does not directly yield an analytical solution, it does show that the optimal control is always bang–bang including possibly a singular arc. First, the minimum time problem is solved for a simple switching law in three dimensional state space. A non-standard, physics-based approach is then employed to obtain an optimal solution for the general problem. The final result is an optimal variable structure feedback controller. This result provides a benchmark which can be used for evaluation of the performance of a practical feedback controller designed via other methods. The practical controller will be designed to support a flexible rotor and achieve robustness and optimally reject disturbance. This result may also be applied to many other applications which contain opposing quadratic actuators. © 1998 John Wiley & Sons Ltd.     

Optimal control of first-order plants by dead-beat techniques

The design of optimal dead-beat controllers for first-order plants is considered. The simple nature of these plants leads to ripple-free step response, a property that is highly desirable. The minimum energy and minimum effort control problems are formulated and solved with an analytical and generalized approach. Analytical expressions for the digital controller pulse transfer functions along with the resulting control sequences are derived. Generalized curves for the maximum control signals appearing in the system are also presented. The latter can be used for the determination of the minimum sampling period when the maximum control signal is specified. © 1997 John Wiley & Sons, Ltd.     

APPROXIMATE OUTPUT FEEDBACK OPTIMAL CONTROL OF HIGHER-ORDER DYNAMICAL SYSTEMS

We present a design methodology for the synthesis of a dynamic controller which minimizes an arbitrary performance index for a non-linear discrete-time system. We assume that only the output of the dynamical system is available for feedback. Consequently, the system input–output relation needs to be represented as a higher-order non-linear difference equation. We show how the optimal control of a higher-order system can be transformed into that of the conventional first-order system and propose a method for constructing the optimal output feedback dynamic controller using modern function approximation techniques. Illustrative examples are presented to demonstrate the effectiveness of the method. © 1997 John Wiley & Sons, Ltd.     

An improved neuro-adaptive-optimal control for oscillation damping in wind-integrated power systems

This article proposes an improved neuro-adaptive-optimal control scheme, based on online system identification and simultaneous control, to replace power system stabilizer in the renewable-energy-penetrated power systems. A simple, linear neural identifier, with a few adjustable connection weights is used, which ensures minimal computational burden, reduced development time, and makes the controller practically realizable. An adaptive learning rate, derived using Lyapunov stability theorems, guarantees stability of convergence of the learning algorithm as well as an optimal speed of convergence. It is demonstrated that a simple linear neural identifier, which approximates a local linear model of a system, by adjustment of its parameters online, is faithfully able to track the varying dynamics of the system. Improved oscillation-damping performance over a wide range of operating conditions and disturbances, in comparison with a well-established IEEE-PSS1A and fuzzy-logic-control-based PSS, was validated through simulation studies on a single-machine infinite-bus power system and a wind-integrated two-area power system. The computational superiority of the proposed scheme in comparison to complex and non-linear neural networks and fuzzy-logic-based control was also established. The novelty of the controller lies in its structure which, in-spite of being purely linear, performs robustly for highly complex and non-linear power system models.